Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome.

ABSTRACT:

Background

Acute kidney injury (AKI) can make cases of acute respiratory distress syndrome (ARDS) more complex, and the combination of the two can significantly worsen the prognosis. Our objective is to utilize machine learning (ML) techniques to construct models that can promptly identify the risk of AKI in ARDS patients.

Method

We obtained data regarding ARDS patients from the Medical Information Mart for Intensive Care III (MIMIC-III) and MIMIC-IV databases. Within the MIMIC-III dataset, we developed 11 ML prediction models. By evaluating various metrics, we visualized the importance of its features using Shapley additive explanations (SHAP). We then created a more concise model using fewer variables, and optimized it using hyperparameter optimization (HPO). The model was

SUBMITTER: Wei S 

PROVIDER: S-EPMC10548692 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature